Programme Overview

Artificial Intelligence (AI) and Big Data make analyzing businesses has become easier through the set of data tools such as Tensor Flow and Hadoop. AI is the most in-demand methodology to solve business problems today. This module will equip students with the ability to apply AI in areas like Human Resource (HR), Marketing, Operation Management, Business Law, Strategic Management. Student will learn to utilise modern development tools to turn information into insights, learn to understand the development environment of AI including cloud-based AI. 

As business needs for data is rapidly changing, companies are faced with increased challenge in scalability while maintaining operational needs. This module will equip the student the understanding of both traditional Relational Database and NoSQL, data warehouse and the ability to plan for both operational and analytical needs. Student will also learn Document, Graph, and Wide-Column store, ETL techniques and system design considerations.

This course is designed for post-graduate business analytics student who had basic structured programming background and is interested to learn how to program effectively with data structures. It is oriented to enhance the student technical skillset. The aim of this course is to provide a broad understanding on data structure and explore the various programming paradigms in greater depth. This course will equip the students with the ability to optimize codes and write libraries for data analysis. This module will provide students with individual hands-on practices to enhance the coding skillset and opportunity to develop modular solution in a team. Python language will be used as the main medium of learning because it is one of the most in-demand coding language and its user-friendly syntax is well suited for mastering the data structure concepts.

Technological advancement renders both opportunity and shortfall for business organization. The difference lies on the business stakeholders’ perspectives and understanding on technologies and discovering the insights from data. Appreciation of analytics and machine learning can often turn the shortfall into opportunity and transforms a business. In this age of technology, the speed for human to catch up with technology is often the key for a business to stay relevant. This course introduces the concept and technology behind analytics and how can business embed and embrace them in its operation.

This course will walk you through different types of analytics and machine learning deployed in major sectors, enabling data-driven strategies for the given organisation. You will learn about the current state of the art of machine learning development and create an AI prototype, enabling you to appreciate how artificial “intelligence” is derived from human intelligence.

Analytics, Data Science and Artificial Intelligence are transforming business, social and government’s way of work and way of life. This course will show how important ideas and concepts were applied in real world applications to change the way we live and work.

The aim of the Data Analytics Practicum is to create sharp business analysis to ensure that right decisions are made. This will enable you to harness the power of data science, Big Data, statistics, and machine learning to optimize results and achieve strategic objectives.

You will work on relevant-industry projects from one of the following categories:

1. Work-study arrangement with organizations and industry partners
2. Any one research laboratory in NTU
3. Faculty-supervised research project

Projects will be mentored by Nanyang Business School instructors and industry mentors.

It is an educational collaboration between partner organizations (sponsors) and the Nanyang Business School, where participants work as a team to apply the latest business thinking and insights from their MSBA modules to address the opportunities and challenges of an industry sponsor. With guidance from course supervisors and sponsors, each participants is expected to speak with relevant industry representatives, gather appropriate data, perform detailed analyses of the collected data and information, create analytics or AI model and arrive at realistic recommendations in the final presentation.

This course presents you with fundamental concepts and techniques in managing and presenting data for effective data-driven decision making. Topics include:

• Data management and design:
  • Data design approaches for performance and availability, such as data storage and indexing strategies
  • Data warehousing, such as requirement analysis, dimensional modelling, and ETL (extract, transform, load) processing.
• Data visualisation and understanding data types:
  • Data dimensionalities, such as time-series and geospatial data
  • Forms of data visualisation to include heat maps
  • Best practices for usable, consumable, and actionable data/analytic presentation

This course introduces you to the concepts and methods of statistical inferences: the process of inferring unknowns based on collected data. You will also learn basic programming skills to conduct statistical analyses in the R environment.

This course consists of three main modules:

• Module 1 introduces basic elements of probability theory
• Module 2 covers confidence intervals and hypothesis testing
• Module 3 introduces two applications of statistical inferences, linear regression, and simulation analysis

Each weekly topic will be supplemented with relevant computer applications in the R environment.

This course is designed for post-graduate business analytics student to acquire the necessary skills to manage data and conduct business analytics programmatically. It is oriented to enhance the student technical skillset. The aim of this course is to provide a broad understanding on programming paradigms, coding techniques, how to manage data, the process of preparing data for analysis, fundamentals of analytics, and the means to communicate analytics outcome. This course will equip the students with the ability to write customized solutions to inform business decision, integrate statistical libraries for data analysis, and construct visuals or reports for business understanding. This module will provide students with individual hands-on practices to hone the coding skillset and opportunity to develop coding solution in a team. Python language will be used as the main medium of learning because it is one of the most in-demand coding language and its user-friendly syntax is well suited for beginner level. Students will be also be exposed to R and JavaScript.

This course presents the fundamentals of data visualization and storytelling for persuasion and effective evidence-based decision making. Topics in this course include understanding the analytics, designing the message, designing appropriate visuals, and developing communication strategies based on business analytics that aim to persuade calls to action.

This course covers the fundamental concepts of time series analysis and should give students a foundation for working with time series data. Topics include univariate ARIMA modeling, model identification and diagnostics, equilibrium correction model, and GARCH model and its applications in volatility estimation.

This is an introductory course designed for students who are interested in finance and insurance analytics. The course is conducted in two components. The first component focuses on how to manage data, conduct business analytics programmatically, create Artificial Intelligence (AI) model to automate business processes and create predictive model to increase profitability or returns from the perspective of finance The second component focuses on insurance analytics and it demonstrates how to use analytics to systematically improve operations ranging from underwriting, pricing, risk management to claims. Emphasis will be on the importance of data in the insurance industry, frequency modelling, loss severity modelling, aggregate risk models, risk classification and risk management of insurance portfolio. Selected case studies will be discussed in the course to enhance students learning and the course is oriented to enhance the individual’s technical skillset. Students are expected to have basic knowledge in statistics, probability and regression.

This course provides fundamental tools for credit risk modelling and evaluation by data analytic techniques. This includes stochastic modelling techniques and statistical approaches to data mining based on decision trees, logistic regression and neural networks.

Course concepts are illustrated by R and Python codes applied to credit rating and scoring.

Innovation is the lifeblood of organizations today, fuelling continued growth and sustainability of the firm.

This course will introduce you to the key innovation skills that leaders and managers need to identify new business opportunities and to drive innovation in their firms. Through a series of highly interactive sessions, you will learn to apply design thinking to identify key customer needs and derive solutions to key problems.

You will also learn to apply analytics to create business models for business ideas, and about the lean start-up approach which advocates the iteration of a business model. Applying these concepts, you will learn to analyse business and market opportunity through testing assumptions embedded in the business model

The course will further discuss the management of the latest technologies impacting the 4th industrial revolution. We discuss concepts of blockchain, examine the blockchain using cases and challenges related to its implementation. We also discuss about cases of firms commercializing artificial intelligence.

This course introduces you to the concepts of business processes and business process redesign, as well as using and managing Information Systems. It will also introduce you to new technologies like the blockchain and robotic process automation.

A firm conceptual foundation in Information Systems is now a necessary pre-requisite for effective performance of any business professional in this information age and digital era. It will enable you to better understand, evaluate, and use Information Systems in a business manager, professional, analyst or consultant role.

This course takes a practical approach with demonstrations in class and with candidates setting up, processing transactions, generating financial reports and analysing data using an industry-leading Enterprise Resource Planning or ERP system, SAP.

This course introduces students to cyber security and help them in developing basic understanding of the common security issues through a general overview of the field, followed by a number of specific topics.

This course covers essential concepts of machine learning and various supervised and unsupervised learning algorithms, such as Support Vector Machines (SVM), K-Nearest Neighbour (K-NN) classifiers, decision tree, K-means clustering, hierarchical clustering etc.

This course also discusses their applications and their weaknesses. 

This course seeks to provide business analytics graduates with a rigorous appreciation of the issues and methodologies necessary for ensuring the competitiveness of the operations function in a firm. The course will be taken by MSBA students as an elective. The course takes an analytics-based “process management’ viewpoint while addressing a range of strategic and tactical issues. After completing this course, you will be able to understand the key tradeoffs required for designing, managing, and improving operations and processes in both manufacturing and service industries. This course will give you a sound analytical background and prepare you for a future business career where you will be responsible for the interface of the operations function with other business functions such as strategy, marketing, finance, accounting, and information technology.

With RPA, businesses can automate mundane rules-based business processes, enabling business users to devote more time to serving customers or other higher-value work.

RPA also allow access to legacy systems which may be difficult to the users in the past. This can transform data collection capabilities of enterprises, especially those that depend on legacy systems.

The objective of this course is to understand the role and impact of technology in managing strategic transformation in organizations. Technology enables firms to offer new products, create new customer channels, and dramatically improve the efficiency of their supply chains. The purpose of this course is to introduce the key issues in managing the firm’s technology resources; and to stress management’s role in creating the “technology-friendly” firm. In addition, the course will provide an overview of key information technologies in use today and how they support a variety of operational and strategic decisions within firms.

The course will focus on two specific aspects. First, in class, we will spend time understanding the common elements of the technology infrastructure in today’s organizations. As a manager, you will be engaged with making some of these crucial investment decisions, and you need to understand what the capabilities of the technology are, as well as the trade-offs involved in selecting one versus another. Second, in order to implement technology that can transform the firm, we need some frameworks and models to guide our thinking. Several of these frameworks and theories will be discussed during the course. The material in the course draws in equal measure from descriptions of technology and value, strategic frameworks, and casework.